from keras import models
from keras import layers

from keras.datasets import mnist
from keras.utils import to_categorical

import numpy as np

def build_network():
	model = models.Sequential()
	model.add(layers.Conv2D(32, (3,3), activation='relu', input_shape=(28,28,1)))
	model.add(layers.MaxPooling2D((2,2)))
	
	model.add(layers.Conv2D(64, (3,3), activation='relu'))
	model.add(layers.MaxPooling2D((2,2)))

	model.add(layers.Conv2D(64, (3,3), activation='relu'))

	model.add(layers.Flatten())
	model.add(layers.Dense(64, activation='relu'))
	model.add(layers.Dense(10, activation='softmax'))

	return model


def train_model(model, epochs=5, batch_size=64):
	(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

	train_images = train_images.reshape((60000,28,28,1))
	train_images = train_images.astype(np.float32) / 255

	test_images = test_images.reshape((10000, 28, 28, 1))
	test_images = test_images.astype(np.float32) / 255

	train_labels = to_categorical(train_labels)
	test_labels = to_categorical(test_labels)

	model.compile(optimizer='rmsprop',
					loss='categorical_crossentropy',
					metrics=['accuracy'])

	model.fit(train_images, train_labels, epochs=epochs, batch_size=batch_size)

	test_loss, test_acc = model.evaluate(test_images, test_labels)

	print('test accuracy is : ' + str(test_acc))

	predict_result = model.predict(test_images[:1])

	print(predict_result)



if __name__ == '__main__':
	model = build_network()

	print(model.summary())

	train_model(model)
